Search Plan Research Articles

Student Digital Literacy Competence Study Language and Literature Education Program Indonesia

This study aims to determine the digital literacy competence of Indonesian Language and Literature Education Study Program students. This study used a descriptive qualitative method to process the data from the sixth-semester student at HKBP Nommensen University. The results of this study found that students had good basic skills, major skills, intermediate skills, advanced skills, and literacy skills. However, the technical capabilities are still relatively poor. In (1) Basic Competencies, some problems are resolved properly, there are also several topical problems that still exist but at least there is an overview of problem-solving. (2) Students' Basic Competencies have a fairly good introduction to the basics of computer equipment. (3) Students' Intermediate Competencies increase at this stage, namely understanding various features on laptops, using printers, and using Ms. applications. Word and use of e-mail applications. The mastery of this competency is due to the fact that informants often carry out these activities so that they are more accustomed to their use. (4) Advanced Competencies to access information using search engines and online databases are good. (5) Technical Competencies of the Indonesian Language and Literature Study Program students as research informants do not yet have technical expertise regarding hardware and software. Informants have not been able to repair damage to digital equipment even at a simple level. (6) Students' Digital Literacy Proficiency does not yet have sufficient literacy skills starting from formulating a search plan, establishing a search strategy, organizing, processing, applying and communicating information, and understanding standard citation formats, and do not have the effort to communicate information with technology.

The maximum entropy principle in search theory

The paper considers the relationship between search theory and information theory. The traditional problem of search theory is to develop a search plan for a physical object in the sea or on land. The search plan has to develop the distribution of available search resources in such a way that the probability of detection the search object is to be maximum. The optimal solution is traditionally considered as so-called "uniformly optimal search plan", which provides a uniform distribution of the posterior probability of the location of the object as the search is conducted. At the same time, optimality simultaneously according to the criteria of maximum detection probability and equality of a posteriori probability is possible only for the exponential detection function, which is used most often in search theory. For other kinds of detection functions, the optimal solutions according to the specified criteria do not match. In this paper, the approach to this problem is considered on the basis of the maximum entropy principle. For a situation of discrete distribution, it is shown that, within the framework of information theory, the search problem has a simpler solution that does not depend on the kind of the detection function.

A Novel Bias-TSP Algorithm for Maritime Patrol

This work aims to develop a search planning strategy to be used by a drone equipped with an inverse synthetic-aperture radar (ISAR) and an electro-optical sensor. After describing the specifics of our maritime scenario, we discuss four methodologies that can be used to find vessels involved in illegal fishing activities as quickly as possible. In addition to the clustering of the vessels, determined by the drone’s electro-optical sensor range, we introduce a novel technique to bias a traveling salesman problem (TSP) tour. This bias is based on deliberately increasing distances to vessels that are classified as probable fishing vessels. This increase in distance is meant to prioritize visits to probable fishing vessels. Vessels are classified based on length using the. The classification result and the vessel clustering are available before the actual planning of the tour. Simulations of scenarios in which we have a few vessels fishing illegally show that the novel technique, the bias-TSP, combined with a tour orientation based on operational considerations, outperforms the classic TSP: the mean distance traveled to find all the vessels involved in illegal fishing activities is reduced by at least 35–50%. We also show that different drone take-off locations significantly impact the results.

A Multirobot Person Search System for Finding Multiple Dynamic Users in Human-Centered Environments.

Multirobot coordination for finding multiple users in an environment can be used in numerous robotic applications, including search and rescue, surveillance/monitoring, and activities of daily living assistance. Existing approaches have limited coordination between robots when generating team plans or do not consider user location probability within these plans. This results in long searches and robots potentially revisiting the same locations in succession. In this article, we present a novel multirobot person search system to generate search plans for multirobot teams to find multiple dynamic users before a deadline. Our approach is unique in that it simultaneously considers the search actions of all robots and user location probabilities when generating team plans, where user location probabilities are represented as conditional spatial-temporal probability density functions. We model this multirobot person search problem as a two-stage optimization problem to maximize the expected number of users found before the deadline. Stage 1 solves the action selection problem to determine a set of team actions, and the second stage solves the action allocation problem to distribute these actions amongst the robots. Namely, in stage 1, a novel conditional multiperiod multiknapsack problem is modeled as a min-flow graph solved sequentially by the Bellman-Ford shortest path algorithm. Stage 2 is a variant of the min-max multitraveling salesperson problem which models the environment topology as a search region network and search times selected by the previous stage. This stage is solved by a novel fuzzy clustering method. Numerous experiments comparing our proposed method to other existing approaches with varying environment sizes, search durations, and the number of users showed that our approach was able to find more target users before a defined deadline.

Theoretical foundation for solving search problems by the method of maximum entropy

The traditional problem of search theory is to develop a search plan for a physical object in the sea or on land. Known algorithms for the optimal distribution of search resources mainly use the exponential detection function. If we consider the search problem more broadly — as a problem of searching for various information, then the detection function can differ significantly from the exponential one. In this case, the solutions obtained using traditional algorithms may be correct from the point of view of mathematics, but unacceptable from the point of view of logic. In this paper, this problem is solved on the basis of the maximum entropy principle. The theorems are proved, as well as their consequences for four types of detection functions, which make it possible to create algorithms for solving various search problems based on the principle of maximum entropy.

Методика емпіричного вивчення стану сформованості прогностичної діяльності в молодших школярів з інтелектуальними порушеннями.

The article reflects the main components of studying the state of formation of prognostic activity in younger schoolchildren with intellectual disabilities: cognitive (is studied by indicators: "the ability to understand the learning task", "the ability to plan one's actions", "the ability to analyze the conditions for completing the task"), personal-regulatory (is investigated according to the indicators: "the ability to develop search planning", "Possession of the ability to predict"). The results of the ascertainment stage of the implementation of a special methodology for the study of prognostic activity in younger schoolchildren with intellectual disabilities are presented, which includes two areas: 1) research on the state of formation of prognostic activity in younger schoolchildren with normotypical development and intellectual disabilities; 2) empirical analysis of the plans and structure of the fine arts lesson as a factor in the formation of prognostic activity in younger schoolchildren with intellectual disabilities. The first direction was the study of the state of formation of prognostic activity of younger schoolchildren with intellectual disabilities. According to the results of the study, almost all students with intellectual disabilities who participated in the research at the ascertainment stage experienced difficulties in completing tasks. A high level of prognostic activity was not found in younger schoolchildren with intellectual disabilities. According to the indicator of development of the ability to understand the educational task, about 30% of students showed an average level, and almost 70% showed a low level. The average level of development of the ability to plan one's actions in the group of younger schoolchildren with intellectual disabilities is determined in almost a quarter of students who participated in the study, a low level is determined in 75.4% of students. The average level of development of the ability to analyze the conditions of the task was determined only in 8.56% of students, a low level was found in the majority of students with intellectual disabilities - more than 90%. This allows us to draw a conclusion about the need for purposeful work with younger schoolchildren with intellectual disabilities regarding the formation of educational activities, understanding of tasks, goal setting, planning and forecasting.

Sources of mapping used in humanitarian emer- gencie: The case of Ebola

The need to rapidly respond to health emergencies has generated various institutional initiatives to identify their location, through mapping. This study employs a qualitative-exploratory method, based on the daily monitoring of eight Ebola epidemics between 2013 and 2021, to make an assessment of the usefulness of maps created specifically for Ebola. The results show that at least 14 organisations produce maps in the face of emergencies and epidemics such as Ebola. Consequently, it has been possible to design a search plan to aid in the monitoring of emergencies and to design an Ebola map with data obtained from these organisations and the initiatives they promote, which confirms the usefulness of these data sources and maps.

Optimizing Searchers that can Transport and Deploy another Searcher Using an Agent Based Model and Nonlinear Optimization Methods in a Maritime Domain

This paper introduces a new methodology, nicknamed Pathfinder, for finding optimal search paths for searchers that can transport and deploy other searchers. The methodology applies an Agent-Based Model to model target movement, then uses nonlinear optimization methods to find optimal search plans. This methodology can optimize these search teams effectively and quickly. Pathfinder significantly increase probability of detection and decreases travel distance. In addition to advancing Search Theory, this methodology also has the potential to enhance current search and rescue (SAR) and anti-submarine warfare (ASW) operations.

Solving the environmental/economic dispatch problem using the hybrid FA-GA multi-objective algorithm

The emissions of polluting gases due to the consumption of fossil fuels in power plants have caused that in addition to operating costs, minimizing the amount of pollution in power plants is also given special attention. In this study, the hybrid firefly algorithm (FA) and genetic algorithm are used to solve the problem of environmental economic dispatch (EED) of power between thermal power plants in order to reduce operating costs and environmental pollution while accounting for the nonlinear constraints of power plants such as the effect of a steam valve, prohibited zones of generation, and generation change rate of plants (GA). Basing on the advantageous of these two optimization algorithms, the proposed application have make a combination between these algorithms and have concluded to have a hybrid FA-GA multi-objective algorithm, which can resolve this complicate optimization problem. The algorithm starts with a set of fireflies that are randomly distributed in the problem space, and these particles converge to the optimal solution of the problem during the evolutionary stages. Then a local search plan is presented and implemented as a way to search the neighborhood to improve the quality of the answers. This part of the algorithm is used to search for sparsely populated areas to find the dominant answers. To improve the algorithm, changes have been made in the criteria for determining the best global optimum for each firefly, as well as the best local optimum. The use of this method has increased the uniformity of the Pareto curve Finally, the suggested algorithm is applied on the 39-bus IEEE system by specifying the indices of losses, voltage stability, and emission of pollutants in the multi-objective problem and its results show its proper performance in comparison with other methods. Actually, the proposed control application have conducted to observe the minimum cost and gaz emission face five other conventional used algorithms.

HeadSpace: Incorporating Action Failure and Character Beliefs into Narrative Planning

Previous work on story planning has lacked a knowledge representation for characters that attempt actions that fail because of the characters' misconceptions about the world state. This work describes HeadSpace, a state-space heuristic search planning system that generates stories that track and manipulate characters' beliefs about the story world. The planner produces story plans with actions that are attempted but fail. We show an example story plan that contains failed-action content that cannot be generated by typical planning-based approaches to story creation, and we provide an analytical evaluation that characterizes our planner's increased expressive range relative to other narrative planners addressing character belief and/or failed action execution.

Impact of cancer types on COVID-19 infection and mortality risk: a protocol for systematic review and meta-analysis

IntroductionThe COVID-19 pandemic has created a huge social and economic burden, and the lifestyles of individuals have significantly changed. In addition, the diagnosis, treatment and management of patients with cancer...

Supervised learning for maritime search operations: An artificial intelligence approach to search efficiency evaluation

We present a metamodeling approach, based on supervised learning, to estimate the probability of success of maritime search and rescue operations. The objective is to improve search planning in a context where lives are at risk and time is of the essence. The proposed approach has been evaluated both in terms of its predictive performance (Can the probability of success be closely approximated?), and of its added value to the decision support system operationally used by the Canadian Coast Guard (To what extent does the approach improve the current system?). We conducted extensive experimentations to evaluate and compare four machine learning algorithms namely, random forest, k-nearest neighbor, support vector machine regression, and feed forward neural networks with a single layer. Our experimental results, based on real-life data, show that the learned models can approximate the probability of success with sufficient precision and that a heuristic, implementing a k-nearest neighbor model within the existing decision support system, can recommend search plans with higher probabilities of success, which has the potential to save more lives.

Wilderness Search for Lost Persons Using a Multimodal Aerial-Terrestrial Robot Team

Mobile robots that are capable of multiple modes of locomotion may have tangible advantages over unimodal robots in unstructured and non-homogeneous environments due to their ability to better adapt to local conditions. This paper specifically considers the use of a team of multimodal robots capable of switching between aerial and terrestrial modes of locomotion for wilderness search and rescue (WiSAR) scenarios. It presents a novel search planning method that coordinates the members of the robotic team to maximize the probability of locating a mobile target in the wilderness, potentially, last seen on an a priori known trail. It is assumed that the search area expands over time and, thus, an exhaustive search is not feasible. Earlier research on search planning methods for heterogeneous though unimodal search teams have exploited synergies between robots with different locomotive abilities through coordination and/or cooperation. Work on multimodal robots, on the other hand, has primarily focused on their mechanical design and low-level control. In contrast, our recent work, presented herein, has two major components: (i) target-motion prediction in the presence of a priori known trails in the wilderness, and (ii) probability-guided multimodal robot search-trajectory generation. For the former sub-problem, the novelty of our work lies in the formulation and use of 3D probability curves to capture target distributions under the influence of a priori known walking/hiking trails. For the latter, the novelty lies in the use of a tree structure to represent the decisions involved in multimodal probability-curve-guided search planning, which enables trajectory generation and mode selection to be optimized simultaneously, for example, via a Monte Carlo tree search technique. Extensive simulations, some of which are included herein, have shown that multimodal robotic search teams, coordinated via the trajectory planning method proposed in this paper, clearly outperform their unimodal counterparts in terms of search success rates.

Maritime search and rescue probability of containment model design and simulation

Abstract In maritime search and rescue operations, in order to improve the success rate of search and rescue, it is necessary to quickly determine the search area after target drift according to the existing accident and Marine environment information, and establish the probability of containment model, to provide information support for subsequent search and rescue planning. AP98 wind pressure drift model is used to model the drowning person at sea, and Monte Carlo simulation is used to simulate the floating situation of the drowning person. A large number of initial hypothetical points were arranged, the factors affecting the drift trajectory were quantitatively analyzed, and the probability density of the hypothetical points in the search area after a certain period of time was counted to form the inclusive probability model. Matlab software is used to test the model in different scenarios to prove the effectiveness of the proposed design model.

General provisions of search tactics

The article considers the peculiarities of the search. Based on the analysis of forensic literature, the concept of «search» is considered. The search is a rather complex investigative (search) action, so we pay attention to the preparation of the investigator for this investigative action. The procedural procedure for conducting a search in criminal proceedings has been revealed.
 The grounds for conducting a search in criminal proceedings have been studied. It is substantiated that the sufficient grounds for conducting a search are the obligatory presence of a set of factual and legal grounds set forth in the decision of the investigating judge. Compliance with the reasonable conditions of the search in criminal proceedings is a guarantee of the admissibility of evidence obtained during this investigative action. Three main procedural documents that take place during a search in criminal proceedings are analyzed: 1) petition; 2) approval; 3) protocol.
 The article examines in detail the tactics of the search, which consists of four stages: preparatory, review, detailed and final. The peculiarities of each of these stages are indicated and it is emphasized that the use of special techniques will ensure an excellent search. In addition, it was noted that a properly designed search plan, selected tactics ensure the success of the search, and as a result of obtaining material evidence relevant to such criminal proceedings, and complete and objective documentation of the search is a key point in obtaining evidentiary facts as a result of this action.
 The process of recording search results has been determined, starting from the very beginning of the investigative (search) action - announcing the decision of the investigative court on the search permit, and continuing until the end - the moment of closing the search report.

Hippo

Hyper-parameter optimization is crucial for pushing the accuracy of a deep learning model to its limits. However, a hyper-parameter optimization job, referred to as a study, involves numerous trials of training a model using different training knobs, and therefore is very computation-heavy, typically taking hours and days to finish. We observe that trials issued from hyper-parameter optimization algorithms often share common hyper-parameter sequence prefixes. Based on this observation, we propose Hippo, a hyper-parameter optimization system that reuses computation across trials to reduce the overall amount of computation significantly. Instead of treating each trial independently as in existing hyper-parameter optimization systems, Hippo breaks down the hyper-parameter sequences into stages and merges common stages to form a tree of stages (a stage tree). Hippo maintains an internal data structure, search plan, to manage the current status and history of a study, and employs a critical path based scheduler to minimize the overall study completion time. Hippo applies to not only single studies but multi-study scenarios as well. Evaluations show that Hippo's stage-based execution strategy outperforms trial-based methods for several models and hyper-parameter optimization algorithms, reducing end-to-end training time by up to 2.76X (3.53x) and GPU-hours by up to 4.81X (6.77x), for single (multiple) studies.

A Coordinated Search Algorithm for a Lost Target on the Plane

Concepts in search theory have developed since World War II. The study of search plans has found considerable interest among searchers due to its wide applications in our life. Searching for lost targets either located or moved is often a time-critical issue, especially when the target is very important . In many commercial and scientific missions at sea, it is of crucial importance to find lost targets underwater. We illustrate a technique known as coordinated search, that completely characterizes the search for a randomly located target on a plane. The idea is to avoid wasting time looking for a missing target. Two searchers or robots start from the center of a circle to search out a lost target, the first searcher looks for the target on the right side of the circular area, and the second one looks for it on the left side. The time taken to detect the target is obtained by assuming the target’s position has a symmetric distribution. The procedures to facilitate the detection of the target are presented as an algorithm and as a flowchart. An application demonstrates the applicability of this search technique and the associated decrease in search cost. Its effectiveness is illustrated by numerical results, which indicates considerable promise.

Expected Value of Asymmetric Coordinated Search Technique for Detecting a Randomly Located Target on the Plane

This paper proposes Asymmetric coordination strategy, we study coordinated search technique for a lost located object on the plan, where there are two searchers beginning their search from the same initial point (0, 0), but both sides do not have the same area. Here we introduce a model of search plan and investigate the expected value of detecting the lost target in both sides to avoid wasting time. We present an algorithm to facilitate the search technique. An illustrative application from real life has been introduced to demonstrate the applicability of this search technique. The effectiveness of this model has been illustrated by numerical results

Study on the existence of the transportation particle tracking model in the interactive medium

This paper assumes that the particle jumps randomly (Guassian jumps) from one point to another along one of the imaginary lines inside the interactive medium. Since this study was done in the space, we consider that the position of the particle at any time [Formula: see text] has a multivariate distribution. The random waiting time of the particle for each Gaussian jump depends on its length. An identical set of programed nanosensors (with unit speed) were used to track this particle. Each line has a sensor that starts the tracking process from the origin. The existence of the necessary conditions which give the optimal search plan and the minimum expected value of the particle detection has been proven. This study is supported by a numerical example.

Towards Automated 3D Search Planning for Emergency Response Missions

The ability to efficiently plan and execute automated and precise search missions using unmanned aerial vehicles (UAVs) during emergency response situations is imperative. Precise navigation between obstacles and time-efficient searching of 3D structures and buildings are essential for locating survivors and people in need in emergency response missions. In this work we address this challenging problem by proposing a unified search planning framework that automates the process of UAV-based search planning in 3D environments. Specifically, we propose a novel search planning framework which enables automated planning and execution of collision-free search trajectories in 3D by taking into account low-level mission constrains (e.g., the UAV dynamical and sensing model), mission objectives (e.g., the mission execution time and the UAV energy efficiency) and user-defined mission specifications (e.g., the 3D structures to be searched and minimum detection probability constraints). The capabilities and performance of the proposed approach are demonstrated through extensive simulated 3D search scenarios.